Sample size determination for the false discovery rate |
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Authors: | Pounds Stan Cheng Cheng |
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Affiliation: | Department of Biostatistics, St Jude Children's Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA. stanley.pounds@stjude.org |
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Abstract: | MOTIVATION: There is not a widely applicable method to determine the sample size for experiments basing statistical significance on the false discovery rate (FDR). RESULTS: We propose and develop the anticipated FDR (aFDR) as a conceptual tool for determining sample size. We derive mathematical expressions for the aFDR and anticipated average statistical power. These expressions are used to develop a general algorithm to determine sample size. We provide specific details on how to implement the algorithm for a k-group (k > or = 2) comparisons. The algorithm performs well for k-group comparisons in a series of traditional simulations and in a real-data simulation conducted by resampling from a large, publicly available dataset. AVAILABILITY: Documented S-plus and R code libraries are freely available from www.stjuderesearch.org/depts/biostats. |
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